通过权重排序与部分重编程，显著降低忆阻器重写次数。
Efficient Reprogramming of Memristive Crossbars for DNNs: Weight Sorting and Bit Stucking
- 将权重分段排序，最大化忆阻器状态复用。
- 仅重编程低位列随机选中的部分忆阻器，减少操作次数。
- 在保持精度损失小于1%的前提下，重写次数降低3.7到21倍。
我们提出一种新方法，以减少在基于比特切片的存内计算忆阻交叉阵列上重编程深度神经网络（DNN）所需次数。该方法针对非易失性存储器耐久性有限的问题，即其可重编程次数受限。为降低重编程需求，我们采用两项技术：(1) 将权重组织为有序区块，调度相似交叉阵列的重编程，最大化忆阻器状态复用；(2) 仅对低位列中随机选择的一小部分忆阻器进行重编程，利用其比特级分布特性，并认识到它们对模型精度影响较小。我们在 ImageNet-1K 数据集上评估了该方法对主流模型的效果。结果表明，ResNet-50 的交叉阵列重编程次数减少 3.7 倍，ViT-Base 减少 21 倍，同时模型精度保持在 1% 以内。
原文摘要 · Abstract (English)
We introduce a novel approach to reduce the number of times required for reprogramming memristors on bit-sliced compute-in-memory crossbars for deep neural networks (DNNs). Our idea addresses the limited non-volatile memory endurance, which restrict the number of times they can be reprogrammed. To reduce reprogramming demands, we employ two techniques: (1) we organize weights into sorted sections to schedule reprogramming of similar crossbars, maximizing memristor state reuse, and (2) we reprogram only a fraction of randomly selected memristors in low-order columns, leveraging their bit-level distribution and recognizing their relatively small impact on model accuracy. We evaluate our approach for state-of-the-art models on the ImageNet-1K dataset. We demonstrate a substantial reduction in crossbar reprogramming by 3.7x for ResNet-50 and 21x for ViT-Base, while maintaining model accuracy within a 1% margin.
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